Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations6753
Missing cells6752
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 MiB
Average record size in memory1.2 KiB

Variable types

Numeric14
Text9
DateTime1
Boolean3
Categorical3

Alerts

windows has constant value "True"Constant
score_rank has constant value "98.0"Constant
average_playtime_2weeks is highly overall correlated with median_playtime_2weeksHigh correlation
average_playtime_forever is highly overall correlated with median_playtime_forever and 4 other fieldsHigh correlation
estimated_owners is highly overall correlated with negative and 3 other fieldsHigh correlation
linux is highly overall correlated with macHigh correlation
mac is highly overall correlated with linuxHigh correlation
median_playtime_2weeks is highly overall correlated with average_playtime_2weeksHigh correlation
median_playtime_forever is highly overall correlated with average_playtime_forever and 1 other fieldsHigh correlation
negative is highly overall correlated with average_playtime_forever and 4 other fieldsHigh correlation
peak_ccu is highly overall correlated with average_playtime_forever and 4 other fieldsHigh correlation
positive is highly overall correlated with average_playtime_forever and 5 other fieldsHigh correlation
recommendations is highly overall correlated with average_playtime_forever and 4 other fieldsHigh correlation
user_score is highly imbalanced (99.8%)Imbalance
score_rank has 6752 (> 99.9%) missing valuesMissing
dlc_count is highly skewed (γ1 = 43.92994005)Skewed
achievements is highly skewed (γ1 = 38.10498283)Skewed
recommendations is highly skewed (γ1 = 52.43812356)Skewed
positive is highly skewed (γ1 = 63.4901772)Skewed
negative is highly skewed (γ1 = 65.41759188)Skewed
average_playtime_forever is highly skewed (γ1 = 24.7584795)Skewed
average_playtime_2weeks is highly skewed (γ1 = 42.83885539)Skewed
median_playtime_forever is highly skewed (γ1 = 47.81752635)Skewed
median_playtime_2weeks is highly skewed (γ1 = 41.35416374)Skewed
peak_ccu is highly skewed (γ1 = 70.88276022)Skewed
item_id has unique valuesUnique
required_age has 6430 (95.2%) zerosZeros
price has 918 (13.6%) zerosZeros
dlc_count has 5082 (75.3%) zerosZeros
metacritic_score has 5140 (76.1%) zerosZeros
achievements has 2546 (37.7%) zerosZeros
recommendations has 3184 (47.1%) zerosZeros
negative has 89 (1.3%) zerosZeros
average_playtime_forever has 1878 (27.8%) zerosZeros
average_playtime_2weeks has 6377 (94.4%) zerosZeros
median_playtime_forever has 1878 (27.8%) zerosZeros
median_playtime_2weeks has 6377 (94.4%) zerosZeros
peak_ccu has 3386 (50.1%) zerosZeros

Reproduction

Analysis started2025-11-11 17:57:19.265027
Analysis finished2025-11-11 17:57:58.234657
Duration38.97 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

item_id
Real number (ℝ)

Unique 

Distinct6753
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4305.8698
Minimum0
Maximum8522
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:57:58.390938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile518.6
Q12251
median4318
Q36378
95-th percentile8028.4
Maximum8522
Range8522
Interquartile range (IQR)4127

Descriptive statistics

Standard deviation2397.13
Coefficient of variation (CV)0.55671214
Kurtosis-1.1732042
Mean4305.8698
Median Absolute Deviation (MAD)2063
Skewness-0.017508184
Sum29077539
Variance5746232.2
MonotonicityStrictly increasing
2025-11-11T17:57:58.542166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85221
 
< 0.1%
85001
 
< 0.1%
84981
 
< 0.1%
84971
 
< 0.1%
84961
 
< 0.1%
84951
 
< 0.1%
84941
 
< 0.1%
84931
 
< 0.1%
84921
 
< 0.1%
84891
 
< 0.1%
Other values (6743)6743
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
131
< 0.1%
141
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
ValueCountFrequency (%)
85221
< 0.1%
85211
< 0.1%
85201
< 0.1%
85191
< 0.1%
85171
< 0.1%
85161
< 0.1%
85151
< 0.1%
85141
< 0.1%
85131
< 0.1%
85101
< 0.1%
Distinct6749
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size444.5 KiB
2025-11-11T17:57:59.001379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length81
Median length57
Mean length17.414779
Min length1

Characters and Unicode

Total characters117602
Distinct characters120
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6745 ?
Unique (%)99.9%

Sample

1st rowCounter-Strike
2nd rowRag Doll Kung Fu
3rd rowSilo 2
4th rowCall of Duty: World at War
5th rowRunespell: Overture
ValueCountFrequency (%)
the1011
 
5.2%
of767
 
4.0%
345
 
1.8%
2275
 
1.4%
edition132
 
0.7%
a131
 
0.7%
and110
 
0.6%
war89
 
0.5%
space83
 
0.4%
383
 
0.4%
Other values (6986)16297
84.3%
2025-11-11T17:57:59.933485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12571
 
10.7%
e10494
 
8.9%
a7425
 
6.3%
o6895
 
5.9%
r6685
 
5.7%
i6126
 
5.2%
n5904
 
5.0%
t5725
 
4.9%
s4809
 
4.1%
l4227
 
3.6%
Other values (110)46741
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)117602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12571
 
10.7%
e10494
 
8.9%
a7425
 
6.3%
o6895
 
5.9%
r6685
 
5.7%
i6126
 
5.2%
n5904
 
5.0%
t5725
 
4.9%
s4809
 
4.1%
l4227
 
3.6%
Other values (110)46741
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12571
 
10.7%
e10494
 
8.9%
a7425
 
6.3%
o6895
 
5.9%
r6685
 
5.7%
i6126
 
5.2%
n5904
 
5.0%
t5725
 
4.9%
s4809
 
4.1%
l4227
 
3.6%
Other values (110)46741
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12571
 
10.7%
e10494
 
8.9%
a7425
 
6.3%
o6895
 
5.9%
r6685
 
5.7%
i6126
 
5.2%
n5904
 
5.0%
t5725
 
4.9%
s4809
 
4.1%
l4227
 
3.6%
Other values (110)46741
39.7%
Distinct1794
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
Minimum1997-06-30 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T17:58:00.160077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:58:00.377614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

required_age
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77994965
Minimum0
Maximum18
Zeros6430
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:00.540291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5047574
Coefficient of variation (CV)4.4935687
Kurtosis16.782795
Mean0.77994965
Median Absolute Deviation (MAD)0
Skewness4.3146755
Sum5267
Variance12.283324
MonotonicityNot monotonic
2025-11-11T17:58:00.684639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
06430
95.2%
17201
 
3.0%
1345
 
0.7%
1843
 
0.6%
1623
 
0.3%
104
 
0.1%
152
 
< 0.1%
142
 
< 0.1%
122
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
06430
95.2%
11
 
< 0.1%
104
 
0.1%
122
 
< 0.1%
1345
 
0.7%
142
 
< 0.1%
152
 
< 0.1%
1623
 
0.3%
17201
 
3.0%
1843
 
0.6%
ValueCountFrequency (%)
1843
 
0.6%
17201
 
3.0%
1623
 
0.3%
152
 
< 0.1%
142
 
< 0.1%
1345
 
0.7%
122
 
< 0.1%
104
 
0.1%
11
 
< 0.1%
06430
95.2%

price
Real number (ℝ)

Zeros 

Distinct111
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7734962
Minimum0
Maximum299.9
Zeros918
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:00.871553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.99
median4.99
Q39.99
95-th percentile19.99
Maximum299.9
Range299.9
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.6791528
Coefficient of variation (CV)1.1165057
Kurtosis198.02562
Mean7.7734962
Median Absolute Deviation (MAD)4.99
Skewness7.5646708
Sum52494.42
Variance75.327692
MonotonicityNot monotonic
2025-11-11T17:58:01.083875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.991144
16.9%
4.99954
14.1%
0918
13.6%
14.99502
 
7.4%
2.99410
 
6.1%
19.99363
 
5.4%
1.99349
 
5.2%
0.99315
 
4.7%
3.99285
 
4.2%
6.99249
 
3.7%
Other values (101)1264
18.7%
ValueCountFrequency (%)
0918
13.6%
0.351
 
< 0.1%
0.4924
 
0.4%
0.51
 
< 0.1%
0.5119
 
0.3%
0.534
 
0.1%
0.5418
 
0.3%
0.562
 
< 0.1%
0.5915
 
0.2%
0.693
 
< 0.1%
ValueCountFrequency (%)
299.91
 
< 0.1%
99.991
 
< 0.1%
79.993
 
< 0.1%
69.991
 
< 0.1%
59.9913
 
0.2%
54.991
 
< 0.1%
49.9912
 
0.2%
44.996
 
0.1%
39.9935
0.5%
37.491
 
< 0.1%

dlc_count
Real number (ℝ)

Skewed  Zeros 

Distinct50
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97749149
Minimum0
Maximum579
Zeros5082
Zeros (%)75.3%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:01.324784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum579
Range579
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.5056152
Coefficient of variation (CV)9.7244993
Kurtosis2358.9783
Mean0.97749149
Median Absolute Deviation (MAD)0
Skewness43.92994
Sum6601
Variance90.356721
MonotonicityNot monotonic
2025-11-11T17:58:01.554702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05082
75.3%
11013
 
15.0%
2249
 
3.7%
3107
 
1.6%
459
 
0.9%
545
 
0.7%
632
 
0.5%
820
 
0.3%
718
 
0.3%
917
 
0.3%
Other values (40)111
 
1.6%
ValueCountFrequency (%)
05082
75.3%
11013
 
15.0%
2249
 
3.7%
3107
 
1.6%
459
 
0.9%
545
 
0.7%
632
 
0.5%
718
 
0.3%
820
 
0.3%
917
 
0.3%
ValueCountFrequency (%)
5791
< 0.1%
3431
< 0.1%
2601
< 0.1%
971
< 0.1%
871
< 0.1%
791
< 0.1%
751
< 0.1%
611
< 0.1%
561
< 0.1%
501
< 0.1%

windows
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
True
6753 
ValueCountFrequency (%)
True6753
100.0%
2025-11-11T17:58:01.723282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

mac
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
False
3937 
True
2816 
ValueCountFrequency (%)
False3937
58.3%
True2816
41.7%
2025-11-11T17:58:01.794099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

linux
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
False
4716 
True
2037 
ValueCountFrequency (%)
False4716
69.8%
True2037
30.2%
2025-11-11T17:58:01.874996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

metacritic_score
Real number (ℝ)

Zeros 

Distinct68
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.043536
Minimum0
Maximum96
Zeros5140
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:02.060020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile80
Maximum96
Range96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.889703
Coefficient of variation (CV)1.8123999
Kurtosis-0.13438447
Mean17.043536
Median Absolute Deviation (MAD)0
Skewness1.3200352
Sum115095
Variance954.17376
MonotonicityNot monotonic
2025-11-11T17:58:02.313306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05140
76.1%
7575
 
1.1%
7371
 
1.1%
7270
 
1.0%
8070
 
1.0%
7066
 
1.0%
7764
 
0.9%
7662
 
0.9%
7461
 
0.9%
6861
 
0.9%
Other values (58)1013
 
15.0%
ValueCountFrequency (%)
05140
76.1%
201
 
< 0.1%
291
 
< 0.1%
322
 
< 0.1%
331
 
< 0.1%
341
 
< 0.1%
352
 
< 0.1%
361
 
< 0.1%
372
 
< 0.1%
383
 
< 0.1%
ValueCountFrequency (%)
963
 
< 0.1%
951
 
< 0.1%
942
 
< 0.1%
933
 
< 0.1%
924
 
0.1%
9113
0.2%
9012
0.2%
8912
0.2%
8817
0.3%
8727
0.4%

achievements
Real number (ℝ)

Skewed  Zeros 

Distinct197
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.017178
Minimum0
Maximum5000
Zeros2546
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:02.568799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q329
95-th percentile65.4
Maximum5000
Range5000
Interquartile range (IQR)29

Descriptive statistics

Standard deviation98.989708
Coefficient of variation (CV)4.3006884
Kurtosis1794.0917
Mean23.017178
Median Absolute Deviation (MAD)12
Skewness38.104983
Sum155435
Variance9798.9624
MonotonicityNot monotonic
2025-11-11T17:58:02.838154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02546
37.7%
12184
 
2.7%
20165
 
2.4%
10150
 
2.2%
15143
 
2.1%
13133
 
2.0%
14117
 
1.7%
16116
 
1.7%
30115
 
1.7%
25109
 
1.6%
Other values (187)2975
44.1%
ValueCountFrequency (%)
02546
37.7%
141
 
0.6%
213
 
0.2%
322
 
0.3%
433
 
0.5%
547
 
0.7%
665
 
1.0%
761
 
0.9%
891
 
1.3%
988
 
1.3%
ValueCountFrequency (%)
50001
< 0.1%
48201
< 0.1%
17821
< 0.1%
15021
< 0.1%
12871
< 0.1%
10951
< 0.1%
10791
< 0.1%
8491
< 0.1%
7092
< 0.1%
6371
< 0.1%

recommendations
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1766
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3472.0179
Minimum0
Maximum3441592
Zeros3184
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:03.090019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median117
Q3487
95-th percentile7106.4
Maximum3441592
Range3441592
Interquartile range (IQR)487

Descriptive statistics

Standard deviation49994.831
Coefficient of variation (CV)14.399358
Kurtosis3393.2397
Mean3472.0179
Median Absolute Deviation (MAD)117
Skewness52.438124
Sum23446537
Variance2.4994831 × 109
MonotonicityNot monotonic
2025-11-11T17:58:03.355747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03184
47.1%
10920
 
0.3%
15818
 
0.3%
14617
 
0.3%
13517
 
0.3%
12716
 
0.2%
11515
 
0.2%
12215
 
0.2%
12114
 
0.2%
10713
 
0.2%
Other values (1756)3424
50.7%
ValueCountFrequency (%)
03184
47.1%
10113
 
0.2%
1027
 
0.1%
1038
 
0.1%
1049
 
0.1%
10511
 
0.2%
10613
 
0.2%
10713
 
0.2%
1088
 
0.1%
10920
 
0.3%
ValueCountFrequency (%)
34415921
< 0.1%
12470511
< 0.1%
7834691
< 0.1%
7254621
< 0.1%
6556871
< 0.1%
4573671
< 0.1%
4353281
< 0.1%
4308871
< 0.1%
4085791
< 0.1%
3750541
< 0.1%
Distinct1524
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size624.2 KiB
2025-11-11T17:58:03.741354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length379
Median length11
Mean length45.626684
Min length2

Characters and Unicode

Total characters308117
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1292 ?
Unique (%)19.1%

Sample

1st row['English', 'French', 'German', 'Italian', 'Spanish - Spain', 'Simplified Chinese', 'Traditional Chinese', 'Korean']
2nd row['English']
3rd row['English']
4th row['English', 'French', 'Italian', 'German', 'Spanish - Spain']
5th row['English']
ValueCountFrequency (%)
english6733
21.1%
3027
9.5%
german2423
 
7.6%
french2269
 
7.1%
spanish2161
 
6.8%
spain2092
 
6.5%
italian1747
 
5.5%
russian1721
 
5.4%
portuguese1282
 
4.0%
chinese1181
 
3.7%
Other values (40)7316
22.9%
2025-11-11T17:58:04.418314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'49288
16.0%
25200
 
8.2%
n24104
 
7.8%
i22771
 
7.4%
a17944
 
5.8%
,17896
 
5.8%
s17577
 
5.7%
h14976
 
4.9%
e13978
 
4.5%
l11490
 
3.7%
Other values (45)92893
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)308117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'49288
16.0%
25200
 
8.2%
n24104
 
7.8%
i22771
 
7.4%
a17944
 
5.8%
,17896
 
5.8%
s17577
 
5.7%
h14976
 
4.9%
e13978
 
4.5%
l11490
 
3.7%
Other values (45)92893
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)308117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'49288
16.0%
25200
 
8.2%
n24104
 
7.8%
i22771
 
7.4%
a17944
 
5.8%
,17896
 
5.8%
s17577
 
5.7%
h14976
 
4.9%
e13978
 
4.5%
l11490
 
3.7%
Other values (45)92893
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)308117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'49288
16.0%
25200
 
8.2%
n24104
 
7.8%
i22771
 
7.4%
a17944
 
5.8%
,17896
 
5.8%
s17577
 
5.7%
h14976
 
4.9%
e13978
 
4.5%
l11490
 
3.7%
Other values (45)92893
30.1%
Distinct380
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size413.6 KiB
2025-11-11T17:58:04.763499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length379
Median length2
Mean length13.700281
Min length2

Characters and Unicode

Total characters92518
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)4.1%

Sample

1st row['English', 'French', 'German', 'Italian', 'Spanish - Spain', 'Simplified Chinese', 'Traditional Chinese', 'Korean']
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
4114
34.1%
english3161
26.2%
german534
 
4.4%
russian498
 
4.1%
spanish455
 
3.8%
spain434
 
3.6%
french409
 
3.4%
chinese352
 
2.9%
italian260
 
2.2%
japanese234
 
1.9%
Other values (28)1614
 
13.4%
2025-11-11T17:58:05.449099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'13912
15.0%
n6952
 
7.5%
i6947
 
7.5%
[6767
 
7.3%
]6767
 
7.3%
s5816
 
6.3%
5312
 
5.7%
h4891
 
5.3%
l4136
 
4.5%
a3863
 
4.2%
Other values (38)27155
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)92518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'13912
15.0%
n6952
 
7.5%
i6947
 
7.5%
[6767
 
7.3%
]6767
 
7.3%
s5816
 
6.3%
5312
 
5.7%
h4891
 
5.3%
l4136
 
4.5%
a3863
 
4.2%
Other values (38)27155
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)92518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'13912
15.0%
n6952
 
7.5%
i6947
 
7.5%
[6767
 
7.3%
]6767
 
7.3%
s5816
 
6.3%
5312
 
5.7%
h4891
 
5.3%
l4136
 
4.5%
a3863
 
4.2%
Other values (38)27155
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)92518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'13912
15.0%
n6952
 
7.5%
i6947
 
7.5%
[6767
 
7.3%
]6767
 
7.3%
s5816
 
6.3%
5312
 
5.7%
h4891
 
5.3%
l4136
 
4.5%
a3863
 
4.2%
Other values (38)27155
29.4%
Distinct5904
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-11-11T17:58:06.149076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3118
Median length725
Mean length149.50141
Min length2

Characters and Unicode

Total characters1009583
Distinct characters152
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5901 ?
Unique (%)87.4%

Sample

1st row[{'title': 'Buy Counter-Strike', 'description': '', 'subs': [{'text': 'Counter-Strike: Condition Zero - $9.99', 'description': '', 'price': 9.99}, {'text': 'Counter-Strike - Commercial License - $9.99', 'description': '', 'price': 9.99}]}]
2nd row[{'title': 'Buy Rag Doll Kung Fu', 'description': '', 'subs': [{'text': 'Rag Doll Kung Fu - $0.99', 'description': '', 'price': 0.99}]}]
3rd row[{'title': 'Buy Silo 2', 'description': '', 'subs': [{'text': 'Silo 2 - $59.99', 'description': '', 'price': 59.99}]}]
4th row[{'title': 'Buy Call of Duty: World at War', 'description': '', 'subs': [{'text': 'Call of Duty: World at War - $19.99', 'description': '', 'price': 19.99}]}]
5th row[{'title': 'Buy Runespell: Overture', 'description': '', 'subs': [{'text': 'Runespell: Overture - $9.99', 'description': '', 'price': 9.99}]}]
ValueCountFrequency (%)
23333
18.0%
description13669
 
10.5%
price7690
 
5.9%
text7686
 
5.9%
title5983
 
4.6%
subs5983
 
4.6%
buy5978
 
4.6%
9.992741
 
2.1%
4.992161
 
1.7%
the2073
 
1.6%
Other values (6968)52677
40.5%
2025-11-11T17:58:06.852198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'136052
 
13.5%
123230
 
12.2%
e59612
 
5.9%
i55931
 
5.5%
t54610
 
5.4%
:43386
 
4.3%
s36767
 
3.6%
r36662
 
3.6%
936531
 
3.6%
o30103
 
3.0%
Other values (142)396699
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1009583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'136052
 
13.5%
123230
 
12.2%
e59612
 
5.9%
i55931
 
5.5%
t54610
 
5.4%
:43386
 
4.3%
s36767
 
3.6%
r36662
 
3.6%
936531
 
3.6%
o30103
 
3.0%
Other values (142)396699
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1009583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'136052
 
13.5%
123230
 
12.2%
e59612
 
5.9%
i55931
 
5.5%
t54610
 
5.4%
:43386
 
4.3%
s36767
 
3.6%
r36662
 
3.6%
936531
 
3.6%
o30103
 
3.0%
Other values (142)396699
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1009583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'136052
 
13.5%
123230
 
12.2%
e59612
 
5.9%
i55931
 
5.5%
t54610
 
5.4%
:43386
 
4.3%
s36767
 
3.6%
r36662
 
3.6%
936531
 
3.6%
o30103
 
3.0%
Other values (142)396699
39.3%
Distinct4728
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size456.1 KiB
2025-11-11T17:58:07.255934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length215
Median length89
Mean length19.681327
Min length2

Characters and Unicode

Total characters132908
Distinct characters162
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3807 ?
Unique (%)56.4%

Sample

1st row['Valve']
2nd row['Mark Healey']
3rd row['Nevercenter Ltd. Co.']
4th row['Treyarch']
5th row['Mystic Box']
ValueCountFrequency (%)
games1411
 
9.3%
studios593
 
3.9%
entertainment297
 
2.0%
studio296
 
1.9%
software234
 
1.5%
inc233
 
1.5%
ltd204
 
1.3%
interactive186
 
1.2%
llc166
 
1.1%
game92
 
0.6%
Other values (5652)11482
75.6%
2025-11-11T17:58:07.818170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'14612
 
11.0%
e9280
 
7.0%
8444
 
6.4%
a7932
 
6.0%
]6758
 
5.1%
[6758
 
5.1%
i6278
 
4.7%
t6184
 
4.7%
o6157
 
4.6%
n5332
 
4.0%
Other values (152)55173
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)132908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'14612
 
11.0%
e9280
 
7.0%
8444
 
6.4%
a7932
 
6.0%
]6758
 
5.1%
[6758
 
5.1%
i6278
 
4.7%
t6184
 
4.7%
o6157
 
4.6%
n5332
 
4.0%
Other values (152)55173
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)132908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'14612
 
11.0%
e9280
 
7.0%
8444
 
6.4%
a7932
 
6.0%
]6758
 
5.1%
[6758
 
5.1%
i6278
 
4.7%
t6184
 
4.7%
o6157
 
4.6%
n5332
 
4.0%
Other values (152)55173
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)132908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'14612
 
11.0%
e9280
 
7.0%
8444
 
6.4%
a7932
 
6.0%
]6758
 
5.1%
[6758
 
5.1%
i6278
 
4.7%
t6184
 
4.7%
o6157
 
4.6%
n5332
 
4.0%
Other values (152)55173
41.5%
Distinct3603
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Memory size449.8 KiB
2025-11-11T17:58:08.202912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length90
Median length64
Mean length18.956316
Min length4

Characters and Unicode

Total characters128012
Distinct characters119
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2830 ?
Unique (%)41.9%

Sample

1st row['Valve']
2nd row['Mark Healey']
3rd row['Nevercenter Ltd. Co.']
4th row['Activision']
5th row['Mystic Box']
ValueCountFrequency (%)
games1233
 
8.5%
studios445
 
3.1%
entertainment425
 
2.9%
ltd289
 
2.0%
interactive250
 
1.7%
inc227
 
1.6%
digital200
 
1.4%
llc178
 
1.2%
studio177
 
1.2%
software142
 
1.0%
Other values (4163)11003
75.5%
2025-11-11T17:58:09.286925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'14057
 
11.0%
e8916
 
7.0%
7819
 
6.1%
a7181
 
5.6%
[6754
 
5.3%
]6754
 
5.3%
i6648
 
5.2%
t6418
 
5.0%
n5389
 
4.2%
o5194
 
4.1%
Other values (109)52882
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)128012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'14057
 
11.0%
e8916
 
7.0%
7819
 
6.1%
a7181
 
5.6%
[6754
 
5.3%
]6754
 
5.3%
i6648
 
5.2%
t6418
 
5.0%
n5389
 
4.2%
o5194
 
4.1%
Other values (109)52882
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)128012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'14057
 
11.0%
e8916
 
7.0%
7819
 
6.1%
a7181
 
5.6%
[6754
 
5.3%
]6754
 
5.3%
i6648
 
5.2%
t6418
 
5.0%
n5389
 
4.2%
o5194
 
4.1%
Other values (109)52882
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)128012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'14057
 
11.0%
e8916
 
7.0%
7819
 
6.1%
a7181
 
5.6%
[6754
 
5.3%
]6754
 
5.3%
i6648
 
5.2%
t6418
 
5.0%
n5389
 
4.2%
o5194
 
4.1%
Other values (109)52882
41.3%
Distinct1636
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size914.6 KiB
2025-11-11T17:58:09.514781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length415
Median length327
Mean length89.661632
Min length2

Characters and Unicode

Total characters605485
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1184 ?
Unique (%)17.5%

Sample

1st row['Multi-player', 'PvP', 'Online PvP', 'Shared/Split Screen PvP', 'Valve Anti-Cheat enabled']
2nd row['Single-player', 'Multi-player']
3rd row[]
4th row['Single-player', 'Multi-player', 'Co-op']
5th row['Single-player', 'Steam Achievements', 'Steam Cloud', 'Stats', 'Steam Leaderboards']
ValueCountFrequency (%)
steam11677
19.2%
single-player6377
 
10.5%
achievements4012
 
6.6%
trading3600
 
5.9%
cards3600
 
5.9%
support3245
 
5.3%
controller3105
 
5.1%
cloud2437
 
4.0%
multi-player1966
 
3.2%
full1853
 
3.0%
Other values (45)19059
31.3%
2025-11-11T17:58:09.893062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'60966
 
10.1%
e56454
 
9.3%
54178
 
8.9%
a38485
 
6.4%
l37515
 
6.2%
r33995
 
5.6%
t32202
 
5.3%
,23789
 
3.9%
S23470
 
3.9%
o21931
 
3.6%
Other values (39)222500
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)605485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'60966
 
10.1%
e56454
 
9.3%
54178
 
8.9%
a38485
 
6.4%
l37515
 
6.2%
r33995
 
5.6%
t32202
 
5.3%
,23789
 
3.9%
S23470
 
3.9%
o21931
 
3.6%
Other values (39)222500
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)605485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'60966
 
10.1%
e56454
 
9.3%
54178
 
8.9%
a38485
 
6.4%
l37515
 
6.2%
r33995
 
5.6%
t32202
 
5.3%
,23789
 
3.9%
S23470
 
3.9%
o21931
 
3.6%
Other values (39)222500
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)605485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'60966
 
10.1%
e56454
 
9.3%
54178
 
8.9%
a38485
 
6.4%
l37515
 
6.2%
r33995
 
5.6%
t32202
 
5.3%
,23789
 
3.9%
S23470
 
3.9%
o21931
 
3.6%
Other values (39)222500
36.7%

genres
Text

Distinct539
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size507.9 KiB
2025-11-11T17:58:10.067432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length171
Median length106
Mean length27.995409
Min length2

Characters and Unicode

Total characters189053
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique248 ?
Unique (%)3.7%

Sample

1st row['Action']
2nd row['Indie']
3rd row['Animation & Modeling']
4th row['Action']
5th row['Adventure', 'Indie', 'RPG']
ValueCountFrequency (%)
indie4505
23.9%
action3037
16.1%
adventure2331
12.3%
casual1899
10.1%
strategy1560
 
8.3%
rpg1227
 
6.5%
simulation1151
 
6.1%
free414
 
2.2%
to414
 
2.2%
play414
 
2.2%
Other values (29)1933
10.2%
2025-11-11T17:58:10.437784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'34708
18.4%
e12442
 
6.6%
12132
 
6.4%
n11555
 
6.1%
i10947
 
5.8%
t10815
 
5.7%
,10609
 
5.6%
a7982
 
4.2%
d6937
 
3.7%
]6753
 
3.6%
Other values (34)64173
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)189053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'34708
18.4%
e12442
 
6.6%
12132
 
6.4%
n11555
 
6.1%
i10947
 
5.8%
t10815
 
5.7%
,10609
 
5.6%
a7982
 
4.2%
d6937
 
3.7%
]6753
 
3.6%
Other values (34)64173
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)189053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'34708
18.4%
e12442
 
6.6%
12132
 
6.4%
n11555
 
6.1%
i10947
 
5.8%
t10815
 
5.7%
,10609
 
5.6%
a7982
 
4.2%
d6937
 
3.7%
]6753
 
3.6%
Other values (34)64173
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)189053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'34708
18.4%
e12442
 
6.6%
12132
 
6.4%
n11555
 
6.1%
i10947
 
5.8%
t10815
 
5.7%
,10609
 
5.6%
a7982
 
4.2%
d6937
 
3.7%
]6753
 
3.6%
Other values (34)64173
33.9%

movies
Text

Distinct5942
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-11-11T17:58:10.624931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3823
Median length3465
Mean length119.23441
Min length2

Characters and Unicode

Total characters805190
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5938 ?
Unique (%)87.9%

Sample

1st row[]
2nd row[]
3rd row[]
4th row['http://cdn.akamai.steamstatic.com/steam/apps/5147/movie_max.mp4?t=1480110865', 'http://cdn.akamai.steamstatic.com/steam/apps/5253/movie_max.mp4?t=1480110878', 'http://cdn.akamai.steamstatic.com/steam/apps/5254/movie_max.mp4?t=1480110891']
5th row[]
ValueCountFrequency (%)
808
 
7.8%
http://cdn.akamai.steamstatic.com/steam/apps/5968/movie_max.mp4?t=14473536154
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/2028840/movie_max.mp4?t=14473581662
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/2037631/movie_max.mp4?t=14473702762
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/2037070/movie_max.mp4?t=14473687872
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/5952/movie_max.mp4?t=14473535872
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/256846287/movie_max.mp4?t=16286475521
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/256809871/movie_max.mp4?t=16056346621
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/256808857/movie_max.mp4?t=16049955381
 
< 0.1%
http://cdn.akamai.steamstatic.com/steam/apps/256793430/movie_max.mp4?t=15953222481
 
< 0.1%
Other values (9547)9547
92.1%
2025-11-11T17:58:10.973242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a76504
 
9.5%
t66941
 
8.3%
m66941
 
8.3%
/57378
 
7.1%
p38252
 
4.8%
s38252
 
4.8%
.38252
 
4.8%
432028
 
4.0%
e28689
 
3.6%
i28689
 
3.6%
Other values (26)333264
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)805190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a76504
 
9.5%
t66941
 
8.3%
m66941
 
8.3%
/57378
 
7.1%
p38252
 
4.8%
s38252
 
4.8%
.38252
 
4.8%
432028
 
4.0%
e28689
 
3.6%
i28689
 
3.6%
Other values (26)333264
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)805190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a76504
 
9.5%
t66941
 
8.3%
m66941
 
8.3%
/57378
 
7.1%
p38252
 
4.8%
s38252
 
4.8%
.38252
 
4.8%
432028
 
4.0%
e28689
 
3.6%
i28689
 
3.6%
Other values (26)333264
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)805190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a76504
 
9.5%
t66941
 
8.3%
m66941
 
8.3%
/57378
 
7.1%
p38252
 
4.8%
s38252
 
4.8%
.38252
 
4.8%
432028
 
4.0%
e28689
 
3.6%
i28689
 
3.6%
Other values (26)333264
41.4%

user_score
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.9 KiB
0
6752 
59
 
1

Length

Max length2
Median length1
Mean length1.0001481
Min length1

Characters and Unicode

Total characters6754
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06752
> 99.9%
591
 
< 0.1%

Length

2025-11-11T17:58:11.084385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T17:58:11.160699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
06752
> 99.9%
591
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
06752
> 99.9%
51
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06752
> 99.9%
51
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06752
> 99.9%
51
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06752
> 99.9%
51
 
< 0.1%
91
 
< 0.1%

score_rank
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing6752
Missing (%)> 99.9%
Memory size369.4 KiB
98.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row98.0

Common Values

ValueCountFrequency (%)
98.01
 
< 0.1%
(Missing)6752
> 99.9%

Length

2025-11-11T17:58:11.241324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T17:58:11.319789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98.01
100.0%

Most occurring characters

ValueCountFrequency (%)
91
25.0%
81
25.0%
.1
25.0%
01
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
91
25.0%
81
25.0%
.1
25.0%
01
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
91
25.0%
81
25.0%
.1
25.0%
01
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
91
25.0%
81
25.0%
.1
25.0%
01
25.0%

positive
Real number (ℝ)

High correlation  Skewed 

Distinct2112
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4775.154
Minimum0
Maximum5764420
Zeros64
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:11.416363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q144
median167
Q3702
95-th percentile9735.2
Maximum5764420
Range5764420
Interquartile range (IQR)658

Descriptive statistics

Standard deviation76996.192
Coefficient of variation (CV)16.124337
Kurtosis4655.0652
Mean4775.154
Median Absolute Deviation (MAD)146
Skewness63.490177
Sum32246615
Variance5.9284136 × 109
MonotonicityNot monotonic
2025-11-11T17:58:11.559935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064
 
0.9%
1863
 
0.9%
653
 
0.8%
2250
 
0.7%
1349
 
0.7%
3249
 
0.7%
1545
 
0.7%
944
 
0.7%
3743
 
0.6%
1043
 
0.6%
Other values (2102)6250
92.6%
ValueCountFrequency (%)
064
0.9%
118
 
0.3%
222
 
0.3%
328
0.4%
435
0.5%
532
0.5%
653
0.8%
737
0.5%
832
0.5%
944
0.7%
ValueCountFrequency (%)
57644201
< 0.1%
11711971
< 0.1%
9649831
< 0.1%
8223261
< 0.1%
7036871
< 0.1%
6194571
< 0.1%
5480471
< 0.1%
5208261
< 0.1%
4757851
< 0.1%
4615671
< 0.1%

negative
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1209
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.41818
Minimum0
Maximum766677
Zeros89
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:11.705347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q120
median61
Q3198
95-th percentile1657
Maximum766677
Range766677
Interquartile range (IQR)178

Descriptive statistics

Standard deviation10159.634
Coefficient of variation (CV)16.064741
Kurtosis4820.1986
Mean632.41818
Median Absolute Deviation (MAD)51
Skewness65.417592
Sum4270720
Variance1.0321817 × 108
MonotonicityNot monotonic
2025-11-11T17:58:11.862274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5104
 
1.5%
7100
 
1.5%
1299
 
1.5%
396
 
1.4%
895
 
1.4%
1195
 
1.4%
995
 
1.4%
490
 
1.3%
1390
 
1.3%
089
 
1.3%
Other values (1199)5800
85.9%
ValueCountFrequency (%)
089
1.3%
156
0.8%
271
1.1%
396
1.4%
490
1.3%
5104
1.5%
687
1.3%
7100
1.5%
895
1.4%
995
1.4%
ValueCountFrequency (%)
7666771
< 0.1%
2101541
< 0.1%
1082231
< 0.1%
987011
< 0.1%
895371
< 0.1%
675061
< 0.1%
625741
< 0.1%
570941
< 0.1%
531351
< 0.1%
482411
< 0.1%

estimated_owners
Categorical

High correlation 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size407.8 KiB
0 - 20000
1927 
20000 - 50000
1476 
50000 - 100000
964 
200000 - 500000
817 
100000 - 200000
790 
Other values (8)
779 

Length

Max length20
Median length19
Mean length12.814157
Min length5

Characters and Unicode

Total characters86534
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10000000 - 20000000
2nd row20000 - 50000
3rd row0 - 20000
4th row2000000 - 5000000
5th row50000 - 100000

Common Values

ValueCountFrequency (%)
0 - 200001927
28.5%
20000 - 500001476
21.9%
50000 - 100000964
14.3%
200000 - 500000817
12.1%
100000 - 200000790
11.7%
500000 - 1000000355
 
5.3%
1000000 - 2000000172
 
2.5%
2000000 - 5000000133
 
2.0%
0 - 049
 
0.7%
5000000 - 1000000039
 
0.6%
Other values (3)31
 
0.5%

Length

2025-11-11T17:58:12.009061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6753
33.3%
200003403
16.8%
500002440
 
12.0%
02025
 
10.0%
1000001754
 
8.7%
2000001607
 
7.9%
5000001172
 
5.8%
1000000527
 
2.6%
2000000305
 
1.5%
5000000172
 
0.8%
Other values (4)101
 
0.5%

Most occurring characters

ValueCountFrequency (%)
054794
63.3%
13506
 
15.6%
-6753
 
7.8%
25345
 
6.2%
53796
 
4.4%
12340
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)86534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
054794
63.3%
13506
 
15.6%
-6753
 
7.8%
25345
 
6.2%
53796
 
4.4%
12340
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)86534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
054794
63.3%
13506
 
15.6%
-6753
 
7.8%
25345
 
6.2%
53796
 
4.4%
12340
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)86534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
054794
63.3%
13506
 
15.6%
-6753
 
7.8%
25345
 
6.2%
53796
 
4.4%
12340
 
2.7%

average_playtime_forever
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1109
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.82097
Minimum0
Maximum68357
Zeros1878
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:12.143514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median132
Q3274
95-th percentile1172.2
Maximum68357
Range68357
Interquartile range (IQR)274

Descriptive statistics

Standard deviation1697.0608
Coefficient of variation (CV)4.690333
Kurtosis856.90275
Mean361.82097
Median Absolute Deviation (MAD)132
Skewness24.75848
Sum2443377
Variance2880015.5
MonotonicityNot monotonic
2025-11-11T17:58:12.500583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01878
27.8%
179
 
1.2%
231
 
0.5%
430
 
0.4%
929
 
0.4%
1129
 
0.4%
328
 
0.4%
627
 
0.4%
726
 
0.4%
1826
 
0.4%
Other values (1099)4570
67.7%
ValueCountFrequency (%)
01878
27.8%
179
 
1.2%
231
 
0.5%
328
 
0.4%
430
 
0.4%
525
 
0.4%
627
 
0.4%
726
 
0.4%
822
 
0.3%
929
 
0.4%
ValueCountFrequency (%)
683571
< 0.1%
681591
< 0.1%
427731
< 0.1%
304841
< 0.1%
270801
< 0.1%
268271
< 0.1%
171581
< 0.1%
166231
< 0.1%
153171
< 0.1%
146961
< 0.1%

average_playtime_2weeks
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct245
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.567007
Minimum0
Maximum19159
Zeros6377
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:12.712035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19159
Range19159
Interquartile range (IQR)0

Descriptive statistics

Standard deviation309.24655
Coefficient of variation (CV)13.703481
Kurtosis2373.6299
Mean22.567007
Median Absolute Deviation (MAD)0
Skewness42.838855
Sum152395
Variance95633.427
MonotonicityNot monotonic
2025-11-11T17:58:12.872397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06377
94.4%
122
 
0.3%
311
 
0.2%
116
 
0.1%
185
 
0.1%
45
 
0.1%
125
 
0.1%
544
 
0.1%
404
 
0.1%
24
 
0.1%
Other values (235)310
 
4.6%
ValueCountFrequency (%)
06377
94.4%
122
 
0.3%
24
 
0.1%
311
 
0.2%
45
 
0.1%
54
 
0.1%
64
 
0.1%
73
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
191591
< 0.1%
100121
< 0.1%
67001
< 0.1%
31691
< 0.1%
31021
< 0.1%
29701
< 0.1%
29691
< 0.1%
25351
< 0.1%
25261
< 0.1%
22971
< 0.1%

median_playtime_forever
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct940
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295.92788
Minimum0
Maximum136629
Zeros1878
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:13.036285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median124
Q3261
95-th percentile754.4
Maximum136629
Range136629
Interquartile range (IQR)261

Descriptive statistics

Standard deviation2521.4428
Coefficient of variation (CV)8.5204636
Kurtosis2533.6442
Mean295.92788
Median Absolute Deviation (MAD)124
Skewness47.817526
Sum1998401
Variance6357673.6
MonotonicityNot monotonic
2025-11-11T17:58:13.186296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01878
27.8%
178
 
1.2%
1135
 
0.5%
332
 
0.5%
21230
 
0.4%
429
 
0.4%
929
 
0.4%
22928
 
0.4%
22027
 
0.4%
227
 
0.4%
Other values (930)4560
67.5%
ValueCountFrequency (%)
01878
27.8%
178
 
1.2%
227
 
0.4%
332
 
0.5%
429
 
0.4%
526
 
0.4%
620
 
0.3%
725
 
0.4%
826
 
0.4%
929
 
0.4%
ValueCountFrequency (%)
1366291
< 0.1%
1362911
< 0.1%
427731
< 0.1%
268271
< 0.1%
210351
< 0.1%
167211
< 0.1%
132181
< 0.1%
127511
< 0.1%
120441
< 0.1%
105611
< 0.1%

median_playtime_2weeks
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct238
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.168666
Minimum0
Maximum19159
Zeros6377
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:13.335175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19159
Range19159
Interquartile range (IQR)0

Descriptive statistics

Standard deviation315.68733
Coefficient of variation (CV)14.240249
Kurtosis2206.4398
Mean22.168666
Median Absolute Deviation (MAD)0
Skewness41.354164
Sum149705
Variance99658.489
MonotonicityNot monotonic
2025-11-11T17:58:13.534461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06377
94.4%
122
 
0.3%
311
 
0.2%
55
 
0.1%
644
 
0.1%
404
 
0.1%
24
 
0.1%
44
 
0.1%
134
 
0.1%
114
 
0.1%
Other values (228)314
 
4.6%
ValueCountFrequency (%)
06377
94.4%
122
 
0.3%
24
 
0.1%
311
 
0.2%
44
 
0.1%
55
 
0.1%
64
 
0.1%
73
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
191591
< 0.1%
100121
< 0.1%
67001
< 0.1%
55031
< 0.1%
50301
< 0.1%
32921
< 0.1%
29701
< 0.1%
29691
< 0.1%
25261
< 0.1%
22971
< 0.1%

peak_ccu
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct424
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347.25485
Minimum0
Maximum825215
Zeros3386
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size52.9 KiB
2025-11-11T17:58:13.687363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile149.4
Maximum825215
Range825215
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10595.642
Coefficient of variation (CV)30.512581
Kurtosis5454.3353
Mean347.25485
Median Absolute Deviation (MAD)0
Skewness70.88276
Sum2345012
Variance1.1226762 × 108
MonotonicityNot monotonic
2025-11-11T17:58:13.835391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03386
50.1%
1894
 
13.2%
2428
 
6.3%
3253
 
3.7%
4185
 
2.7%
5132
 
2.0%
6113
 
1.7%
777
 
1.1%
871
 
1.1%
949
 
0.7%
Other values (414)1165
 
17.3%
ValueCountFrequency (%)
03386
50.1%
1894
 
13.2%
2428
 
6.3%
3253
 
3.7%
4185
 
2.7%
5132
 
2.0%
6113
 
1.7%
777
 
1.1%
871
 
1.1%
949
 
0.7%
ValueCountFrequency (%)
8252151
< 0.1%
1705271
< 0.1%
982151
< 0.1%
961121
< 0.1%
602701
< 0.1%
541841
< 0.1%
483521
< 0.1%
456631
< 0.1%
444491
< 0.1%
424421
< 0.1%

Interactions

2025-11-11T17:57:55.862015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:32.494217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:34.595610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:36.431576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:38.539765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:40.475029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:42.623400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:46.038650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:49.056985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:51.520935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:53.456776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:55.975501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:24.044510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:26.863115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:29.837131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:32.829185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:34.719405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:36.552623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:38.664547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:40.603018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:42.818540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:46.234401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:49.196083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:56.103692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:24.167975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:27.048945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:30.034291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:32.972115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:34.855498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:36.687105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:38.808486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:40.734497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:43.015379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:46.456602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:49.336934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:51.790196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:53.731022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:56.242468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:24.290877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:27.241341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:30.227880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:33.092831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:34.993927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:36.801239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:38.945012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:40.863241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:43.202758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:33.223461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:36.932582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:39.077662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:40.987498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:43.410155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:46.897754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:49.857286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:52.055884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:54.005088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:56.486614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:27.669261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:30.626986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:33.364707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:35.241929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:37.061535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:39.227390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:41.119469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:43.600283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:50.004665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:52.191773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:54.143887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:56.596146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:24.750218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:27.867087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:30.851444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:33.503756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:35.377534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:37.190364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:39.353128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:37.319193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:39.494396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:43.989700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:25.109535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:31.266496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:33.767868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:35.622828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:37.726182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:39.625213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:41.516165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:44.199505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:47.742642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:50.429191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:52.599339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:54.984830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:56.992965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:25.305081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:28.504363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:31.472259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:31.682174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:35.879960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:37.989684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:39.904784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:41.825799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:25.681965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:28.961969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:31.843026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:36.033193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:40.044755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:42.033129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:45.354715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:48.444924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:34.336059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T17:57:53.184212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:55.585810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:57.520055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:26.514665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:29.421597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:32.345709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:34.472920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:36.299952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:38.422997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:40.343899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:42.449946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:45.839864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:48.895130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:51.389232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:53.320431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T17:57:55.725988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T17:58:13.959799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
achievementsaverage_playtime_2weeksaverage_playtime_foreverdlc_countestimated_ownersitem_idlinuxmacmedian_playtime_2weeksmedian_playtime_forevermetacritic_scorenegativepeak_ccupositivepricerecommendationsrequired_ageuser_score
achievements1.0000.1240.2730.3100.097-0.1030.0270.0000.1240.2600.1730.2150.1860.2890.2070.2750.0400.000
average_playtime_2weeks0.1241.0000.3170.1740.092-0.0760.0000.0001.0000.2110.2250.3400.3910.3630.1370.3500.1600.000
average_playtime_forever0.2730.3171.0000.2520.428-0.1270.0000.0000.3170.9480.2710.5500.5300.5910.1320.5050.1190.000
dlc_count0.3100.1740.2521.0000.089-0.0660.0140.0000.1740.2220.2100.2590.2570.3100.2170.2840.0790.000
estimated_owners0.0970.0920.4280.0891.0000.1150.1220.0880.0760.0000.1850.6170.6210.6950.0510.6080.1220.000
item_id-0.103-0.076-0.127-0.0660.1151.0000.1090.103-0.076-0.113-0.245-0.280-0.166-0.249-0.206-0.259-0.0770.000
linux0.0270.0000.0000.0140.1220.1091.0000.6540.0000.0000.0910.0000.0040.0230.0210.0410.0420.000
mac0.0000.0000.0000.0000.0880.1030.6541.0000.0000.0000.0740.0000.0170.0260.0210.0280.0610.000
median_playtime_2weeks0.1241.0000.3170.1740.076-0.0760.0000.0001.0000.2110.2250.3400.3910.3630.1370.3500.1600.000
median_playtime_forever0.2600.2110.9480.2220.000-0.1130.0000.0000.2111.0000.2140.4640.4370.5070.1400.4430.0940.000
metacritic_score0.1730.2250.2710.2100.185-0.2450.0910.0740.2250.2141.0000.3990.3650.4350.2990.4710.1960.000
negative0.2150.3400.5500.2590.617-0.2800.0000.0000.3400.4640.3991.0000.6230.8570.1260.7350.2050.000
peak_ccu0.1860.3910.5300.2570.621-0.1660.0040.0170.3910.4370.3650.6231.0000.7310.2440.6760.1720.000
positive0.2890.3630.5910.3100.695-0.2490.0230.0260.3630.5070.4350.8570.7311.0000.1960.8410.2010.000
price0.2070.1370.1320.2170.051-0.2060.0210.0210.1370.1400.2990.1260.2440.1961.0000.3450.1130.000
recommendations0.2750.3500.5050.2840.608-0.2590.0410.0280.3500.4430.4710.7350.6760.8410.3451.0000.2240.000
required_age0.0400.1600.1190.0790.122-0.0770.0420.0610.1600.0940.1960.2050.1720.2010.1130.2241.0000.057
user_score0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0571.000

Missing values

2025-11-11T17:57:57.745231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T17:57:58.043787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

item_iditem_namerelease_daterequired_agepricedlc_countwindowsmaclinuxmetacritic_scoreachievementsrecommendationssupported_languagesfull_audio_languagespackagesdeveloperspublisherscategoriesgenresmoviesuser_scorescore_rankpositivenegativeestimated_ownersaverage_playtime_foreveraverage_playtime_2weeksmedian_playtime_forevermedian_playtime_2weekspeak_ccu
00Counter-StrikeNov 1, 2000010.00TrueTrueTrue880122770['English', 'French', 'German', 'Italian', 'Spanish - Spain', 'Simplified Chinese', 'Traditional Chinese', 'Korean']['English', 'French', 'German', 'Italian', 'Spanish - Spain', 'Simplified Chinese', 'Traditional Chinese', 'Korean'][{'title': 'Buy Counter-Strike', 'description': '', 'subs': [{'text': 'Counter-Strike: Condition Zero - $9.99', 'description': '', 'price': 9.99}, {'text': 'Counter-Strike - Commercial License - $9.99', 'description': '', 'price': 9.99}]}]['Valve']['Valve']['Multi-player', 'PvP', 'Online PvP', 'Shared/Split Screen PvP', 'Valve Anti-Cheat enabled']['Action'][]0NaN198387513510000000 - 2000000010524173322873313230
11Rag Doll Kung FuOct 12, 200501.00TrueFalseFalse6900['English'][][{'title': 'Buy Rag Doll Kung Fu', 'description': '', 'subs': [{'text': 'Rag Doll Kung Fu - $0.99', 'description': '', 'price': 0.99}]}]['Mark Healey']['Mark Healey']['Single-player', 'Multi-player']['Indie'][]0NaN552320000 - 500001501501
22Silo 2Dec 19, 2012060.00TrueTrueFalse000['English'][][{'title': 'Buy Silo 2', 'description': '', 'subs': [{'text': 'Silo 2 - $59.99', 'description': '', 'price': 59.99}]}]['Nevercenter Ltd. Co.']['Nevercenter Ltd. Co.'][]['Animation & Modeling'][]0NaN57250 - 2000000003
33Call of Duty: World at WarNov 18, 20081720.00TrueFalseFalse83035258['English', 'French', 'Italian', 'German', 'Spanish - Spain'][][{'title': 'Buy Call of Duty: World at War', 'description': '', 'subs': [{'text': 'Call of Duty: World at War - $19.99', 'description': '', 'price': 19.99}]}]['Treyarch']['Activision']['Single-player', 'Multi-player', 'Co-op']['Action']['http://cdn.akamai.steamstatic.com/steam/apps/5147/movie_max.mp4?t=1480110865', 'http://cdn.akamai.steamstatic.com/steam/apps/5253/movie_max.mp4?t=1480110878', 'http://cdn.akamai.steamstatic.com/steam/apps/5254/movie_max.mp4?t=1480110891']0NaN3843933632000000 - 5000000255821100226595
413Runespell: OvertureJul 20, 2011010.00TrueFalseFalse6935194['English'][][{'title': 'Buy Runespell: Overture', 'description': '', 'subs': [{'text': 'Runespell: Overture - $9.99', 'description': '', 'price': 9.99}]}]['Mystic Box']['Mystic Box']['Single-player', 'Steam Achievements', 'Steam Cloud', 'Stats', 'Steam Leaderboards']['Adventure', 'Indie', 'RPG'][]0NaN2729350000 - 10000072011703
514Dead Mountaineer's HotelOct 28, 201108.00TrueFalseFalse000['English', 'German', 'Russian'][][{'title': "Buy Dead Mountaineer's Hotel", 'description': '', 'subs': [{'text': "Dead Mountaineer's Hotel - $7.99", 'description': '', 'price': 7.99}]}]['Electronic Paradise']['Akella']['Single-player']['Adventure'][]0NaN356620000 - 5000000000
616Vertex DispenserJun 10, 2011010.00TrueTrueFalse70120['English'][][{'title': 'Buy Vertex Dispenser', 'description': '', 'subs': [{'text': 'Vertex Dispenser - $9.99', 'description': '', 'price': 9.99}, {'text': 'Vertex Dispenser - Four Pack - $29.99', 'description': '', 'price': 29.99}]}]['Michael Brough']['Michael Brough']['Single-player', 'Multi-player', 'Co-op', 'Cross-Platform Multiplayer', 'Steam Achievements', 'Steam Cloud', 'Stats']['Action', 'Indie', 'Strategy'][]0NaN2290 - 200002002000
717PT Boats: Knights of the SeaOct 28, 201107.00TrueFalseFalse000['English'][][{'title': 'Buy PT Boats: Knights of the Sea', 'description': '', 'subs': [{'text': 'PT Boats: Knights of the Sea - $6.99', 'description': '', 'price': 6.99}, {'text': 'PT Boats Gold - $10.99', 'description': '', 'price': 10.99}]}]['Studio4']['Akella']['Single-player', 'Multi-player']['Simulation'][]0NaN19190 - 2000000001
819PT Boats: South GambitOct 28, 201107.00TrueFalseFalse000['English'][][{'title': 'Buy PT Boats: South Gambit', 'description': '', 'subs': [{'text': 'PT Boats: South Gambit - $6.99', 'description': '', 'price': 6.99}, {'text': 'PT Boats Gold - $10.99', 'description': '', 'price': 10.99}]}]['studio4']['Akella']['Single-player', 'Multi-player']['Simulation'][]0NaN12130 - 2000000000
920Orcs Must Die!Oct 11, 2011010.02TrueFalseFalse83275406['English', 'German', 'French', 'Italian', 'Spanish - Spain', 'Russian', 'Japanese', 'Polish', 'Portuguese - Brazil']['English', 'German', 'French', 'Italian', 'Spanish - Spain', 'Russian', 'Japanese', 'Polish', 'Portuguese - Brazil'][{'title': 'Buy Orcs Must Die!', 'description': '', 'subs': [{'text': 'Orcs Must Die! - $9.99', 'description': '', 'price': 9.99}]}]['Robot Entertainment']['Robot Entertainment']['Single-player', 'Steam Achievements', 'Partial Controller Support', 'Steam Cloud', 'Stats', 'Steam Leaderboards']['Action', 'Indie', 'Strategy']['http://cdn.akamai.steamstatic.com/steam/apps/256729815/movie_max.mp4?t=1537484192']0NaN67792511000000 - 20000003730240025
item_iditem_namerelease_daterequired_agepricedlc_countwindowsmaclinuxmetacritic_scoreachievementsrecommendationssupported_languagesfull_audio_languagespackagesdeveloperspublisherscategoriesgenresmoviesuser_scorescore_rankpositivenegativeestimated_ownersaverage_playtime_foreveraverage_playtime_2weeksmedian_playtime_forevermedian_playtime_2weekspeak_ccu
67438510Yar's RevengeApr 28, 2011010.00TrueFalseFalse56120['English', 'French', 'German'][][{'title': "Buy Yar's Revenge", 'description': '', 'subs': [{'text': "Yar's Revenge - $9.99", 'description': '', 'price': 9.99}]}]['Killspace Entertainment']['Atari']['Single-player', 'Multi-player', 'Shared/Split Screen', 'Steam Achievements', 'Partial Controller Support', 'Remote Play Together']['Action'][]0NaN42250 - 200001801800
67448513Renegade OpsOct 26, 2011015.02TrueFalseFalse7616778['English', 'French', 'German', 'Italian', 'Spanish - Spain'][][{'title': 'Buy Renegade Ops', 'description': '', 'subs': [{'text': 'Renegade Ops - $15.00', 'description': '', 'price': 15.0}, {'text': 'Renegade Ops Collection - $16.99', 'description': '', 'price': 16.99}]}]['Avalanche Studios']['SEGA']['Single-player', 'Multi-player', 'Co-op', 'Shared/Split Screen', 'Steam Achievements', 'Full controller support', 'Steam Leaderboards', 'Remote Play on TV', 'Remote Play Together']['Action'][]0NaN2151373500000 - 100000093012104
67458514Blade KittenMay 22, 201403.03TrueFalseFalse5220842['English', 'French', 'German', 'Italian', 'Spanish - Spain']['English', 'Spanish - Spain'][{'title': 'Buy Blade Kitten', 'description': '', 'subs': [{'text': 'Blade Kitten - $2.99', 'description': '', 'price': 2.99}, {'text': 'Blade Kitten: Hollow Wish Collection - $6.99', 'description': '', 'price': 6.99}]}]['Krome Studios']['Krome Studios']['Single-player', 'Steam Achievements', 'Steam Trading Cards', 'Partial Controller Support', 'Steam Cloud']['Action', 'Adventure']['http://cdn.akamai.steamstatic.com/steam/apps/2036823/movie_max.mp4?t=1447368141', 'http://cdn.akamai.steamstatic.com/steam/apps/5902/movie_max.mp4?t=1447353494']0NaN830176100000 - 200000166019501
67468515Garshasp: The Monster SlayerMay 9, 201105.00TrueFalseFalse4936217['English', 'German', 'Polish', 'Russian']['English', 'German', 'Polish', 'Russian'][{'title': 'Buy Garshasp: The Monster Slayer', 'description': '', 'subs': [{'text': 'Garshasp: The Monster Slayer - $4.99', 'description': '', 'price': 4.99}]}]['Dead Mage']['Digital Dragon']['Single-player', 'Steam Achievements', 'Partial Controller Support']['Action', 'Indie'][]0NaN17118350000 - 10000000001
67478516Garshasp: Temple of the DragonSep 24, 201205.00TrueFalseFalse000['English']['English'][{'title': 'Buy Garshasp: Temple of the Dragon', 'description': '', 'subs': [{'text': 'Garshasp: Temple of the Dragon - $4.99', 'description': '', 'price': 4.99}]}]['Dead Mage']['Digital Dragon']['Single-player', 'Partial Controller Support']['Action', 'Indie'][]0NaN419020000 - 5000000000
67488517Haunted HouseOct 12, 2023020.00TrueFalseFalse0250['English', 'German', 'Spanish - Spain', 'Portuguese - Brazil'][][{'title': 'Buy Haunted House', 'description': '', 'subs': [{'text': 'Haunted House - $19.99', 'description': '', 'price': 19.99}]}]['Orbit Studio']['Atari']['Single-player', 'Steam Achievements', 'Full controller support', 'Steam Trading Cards', 'Steam Cloud']['Action', 'Adventure']['http://cdn.akamai.steamstatic.com/steam/apps/256964513/movie_max.mp4?t=1692381212']0NaN1220 - 2000000000
67498519NightSkyMar 1, 2011010.00TrueTrueTrue7822263['English'][][{'title': 'Buy NightSky', 'description': '', 'subs': [{'text': 'NightSky - $9.99', 'description': '', 'price': 9.99}]}]['Nicalis, Inc.']['Nicalis, Inc.']['Single-player']['Casual', 'Indie', 'Strategy'][]0NaN821112200000 - 5000004703400
67508520The UnderGardenNov 10, 201001.40TrueFalseFalse6700['English', 'French', 'German'][][{'title': 'Buy The UnderGarden', 'description': '', 'subs': [{'text': 'The UnderGarden - $6.99 $1.39', 'description': '', 'price': 1.39}]}]['Artech Studios']['Retroism']['Single-player']['Casual'][]0NaN542920000 - 500002102100
67518521Spiral KnightsJun 14, 201100.01TrueTrueFalse6454191['English', 'French', 'German', 'Spanish - Spain'][][{'title': 'Buy Spiral Knights', 'description': '', 'subs': [{'text': 'Spiral Knights: Guardians Armor Pack - $39.99', 'description': '', 'price': 39.99}]}]['Grey Havens']['Grey Havens']['Single-player', 'Multi-player', 'MMO', 'Co-op', 'Steam Achievements', 'Steam Trading Cards', 'In-App Purchases', 'Partial Controller Support']['Action', 'Adventure', 'Casual', 'Free to Play', 'Indie', 'Massively Multiplayer', 'RPG']['http://cdn.akamai.steamstatic.com/steam/apps/2029772/movie_max.mp4?t=1447359212', 'http://cdn.akamai.steamstatic.com/steam/apps/2029059/movie_max.mp4?t=1447358466', 'http://cdn.akamai.steamstatic.com/steam/apps/2028130/movie_max.mp4?t=1654550580', 'http://cdn.akamai.steamstatic.com/steam/apps/80868/movie_max.mp4?t=1447354244', 'http://cdn.akamai.steamstatic.com/steam/apps/2028269/movie_max.mp4?t=1447357215']0NaN1888836172000000 - 500000020350920195
67528522Puzzle PiratesAug 31, 201100.00TrueTrueFalse02200['English', 'German', 'Spanish - Spain'][][]['Grey Havens']['Grey Havens']['Single-player', 'Multi-player', 'MMO', 'PvP', 'Online PvP', 'Co-op', 'Online Co-op', 'Steam Achievements', 'In-App Purchases']['Adventure', 'Casual', 'Free to Play', 'Massively Multiplayer', 'Strategy'][]0NaN1394340200000 - 50000036005400119